When we expanded Delphidata's coverage beyond energy transition sectors into defense and space, we learned something quickly: the data models we'd built for hydrogen and CCUS didn't transfer directly. Not because the underlying technology — graph databases, entity resolution, signal pipelines — was wrong, but because the market structure is fundamentally different.
Energy transition markets are organized around projects. A hydrogen production facility, a solar farm, a battery storage system — each is a discrete investment that can be described by its capacity, technology, location, developer, and timeline. The intelligence questions center on "what's being built, where, by whom, and when."
Defense and space markets are organized around programs, procurement relationships, and supply chain networks. The intelligence questions are different: who supplies what to whom, how deep does the supply chain go, where are the single points of failure, and which companies are positioned to capture the next generation of contracts.
Building useful intelligence for these sectors required rethinking our data model from the entity types up.
Programs, not projects
In energy markets, the fundamental unit of analysis is the project. In defense, it's the program.
A defense program — the F-35 Joint Strike Fighter, the Copernicus Earth observation constellation, a national cyber defense initiative — is a multi-year, multi-stakeholder undertaking that generates thousands of discrete contracts, subcontracts, and procurement actions over its lifetime. Tracking it as a single "project" misses the structure that matters.
We model defense and space programs as hierarchical entities. The program itself is the parent node. Beneath it are work packages, contract awards, and procurement actions — each with their own attributes (value, performer, scope, timeline) and their own relationships to companies in the supply chain.
Supply chain depth is the hard problem
In energy, supply chain questions typically go one or two levels deep. Who manufactures the electrolyzer? Who supplies the membrane for the electrolyzer? That's usually enough for most commercial decisions.
In defense and aerospace, supply chain depth is the central intelligence problem. A fighter aircraft involves thousands of components from hundreds of suppliers across multiple tiers. The prime contractor knows its Tier 1 suppliers. But Tier 2, Tier 3, and beyond — the companies making the specialized alloys, the precision bearings, the radiation-hardened electronics — are often opaque even to the program offices that depend on them.
We structure defense supply chain data as a multi-tier graph. Prime contractors link to Tier 1 suppliers, which link to Tier 2, and so on — as deep as the data allows. Each company node carries attributes: capabilities, certifications, production locations, ownership structure, financial indicators. Each supply relationship carries attributes: component type, program context, contract vehicle.
The space economy is a new data domain
Space has its own intelligence challenges. The sector is transitioning from a government-procurement model to a commercial-industrial model, but the data infrastructure hasn't kept up.
Launch providers, satellite manufacturers, ground segment operators, component suppliers, payload developers — each operates in a market that's partially commercial, partially institutional, and increasingly globalized.
We model the space economy with entity types specific to the sector: launch vehicles, satellites and constellations, ground stations, spaceports, in-space infrastructure. Each links to the companies involved and the programs that fund them.
Procurement intelligence is the commercial use case
The most immediate commercial application of defense and space data is procurement intelligence. Companies selling into these sectors need to know: which programs are entering procurement phases, what the requirements look like, who the incumbents are, and where opportunities exist.
Structured data changes the equation. When every contract award, request for proposal, and program milestone is captured as a node in the graph — linked to the program it belongs to, the agencies involved, and the companies participating — patterns become visible that manual monitoring would miss.
Where this is going
Defense and space intelligence is where energy transition intelligence was five years ago: fragmented across government databases, industry publications, company announcements, and institutional knowledge held by individual analysts. The sector is large enough, complex enough, and commercially important enough to justify dedicated data infrastructure.
We're building that infrastructure with the same approach that works in energy — signal pipelines, entity resolution, industrial ontology, graph-based modeling — but with entity types, relationships, and attributes designed for how defense and space markets actually work.